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Heck, D (2004). Proximity Graphs For Nonlinear Dimension Reduction. University of Heidelberg

Bailoni, A, Pape, C, Wolf, S, Kreshuk, A and Hamprecht, F A (2020). Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR. Springer. 12544 331-344

Schiegg, M, Heuer, B, Haubold, C, Wolf, S, Köthe, U and Hamprecht, F A (2015). Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models. ISBI. Proceedings. 394-398

Technical Report (648.55 KB)

Distributions, L (2014). Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. 9–11

Rother, C, Carlsson, S and Tell, D (2002). Projective factorization of planes and cameras in multiple views. Proceedings - International Conference on Pattern Recognition. 16 737–740

Görlitz, L, Menze, B H, Kelm, B Michael and Hamprecht, F A (2009). Processing Spectral Data. Surface and Interface Analysis. 41 636-644

Technical Report (4.17 MB)

Trittler, S (2007). Processing of Interferometric Data. University of Heidelberg

Chellappa, R and Machinery., Afor Comput (2010). Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010. ACM International Conference Proceeding Series. ACM

E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems

Schellewald, C and Schnörr, C (2005). Probabilistic Subgraph Matching Based on Convex Relaxation. Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 171-186

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794

Technical Report (4.07 MB)

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794

Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCV

Technical Report (2.95 MB)

Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCV

Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. ICCV, Proceedings. 2611 - 2618

Technical Report (8.18 MB)

Rathke, F (2015). Probabilistic Graphical Models for Medical Image Segmentation. University Heidelberg

Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480–1492. http://research.microsoft.com/vision/cambridge

Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc.~SSVM. Springer

Technical Report (1.1 MB)

Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic correlation clustering and image partitioning using perturbed Multicuts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9087 231–242

Kappes, J, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc. SSVM. Springer

Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University

Geese, M, Ruhnau, P and Jähne, B (2013). PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics. tm --- Technisches Messen. 80 321--328

Weber, S, Schüle, T and Schnörr, C (2005). Prior Learning and Convex-Concave Regularization of Binary Tomography. Electr. Notes in Discr. Math. 20 313-327

Jähne, (2008). Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse. QZ. 53 45--48

Jähne, B, Scharr, H, Körkel, S, Jähne, B, Haußecker, H and Geißler, P (1999). Principles of Filter Design. Handbook of Computer Vision and Applications. Academic Press. 2 125--151

Jäger, M and Hamprecht, F A (2008). Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes. IEEE Transactions on Industrial Electronics. 56:4 1307-1313

Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020

PDF (1.04 MB)

Detert, M, Jirka, G H, Jehle, M, Klar, M, Jähne, B, Köhler, H - J and Wenka, T (2004). Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow. Proc. of River Flow 2004. A. A. Balkema Publishers. 695-701

Hamprecht, F A, Jost, D, Rüttimann, M, Calamai, F and Kowalski, J J (2001). Preliminary results on the prediction of countershock success with fibrillation power. Resuscitation. 50 297-299

Jähne, (1997). Practical Handbook On Image Processing For Scientific Applications. CRC-Press, Boca Raton, FL, USA

Jähne, (2004). Practical Handbook On Image Processing For Scientific And Technical Applications, 2nd ed. CRC Press

Kondermann, C, Kondermann, D and Garbe, C S (2008). Postprocessing of optical flows via surface measures and motion inpainting. Pattern Recognition. 5096 355--364

Kondermann, C (2009). Postprocessing and Restoration of Optical Flows. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9681

Kondermann, C (2009). Postprocessing and Restoration of Optical Flows. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg

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